Project description:ImportanceAltered heart rate variability has been associated with autonomic dysfunction in a number of disease profiles, in this work we elucidate differences in the biomarker among patients with all-cause sepsis and coronavirus disease 2019.ObjectivesTo measure heart rate variability metrics in critically ill coronavirus disease 2019 patients with comparison to all-cause critically ill sepsis patients.Design setting and participantsRetrospective analysis of coronavirus disease 2019 patients admitted to an ICU for at least 24 hours at any of Emory Healthcare ICUs between March 2020 and April 2020 up to 5 days of ICU stay. The comparison group was a cohort of all-cause sepsis patients prior to coronavirus disease 2019 pandemic.Main outcomes and measuresContinuous waveforms were captured from the patient monitor. The electrocardiogram was then analyzed for each patient over a 300 seconds observational window that was shifted by 30 seconds in each iteration from admission till discharge. A total of 23 heart rate variability metrics were extracted in each iteration. We use the Kruskal-Wallis and Steel-Dwass tests (p < 0.05) for statistical analysis and interpretations of heart rate variability multiple measures.ResultsA total of 141 critically ill coronavirus disease 2019 patients met inclusion criteria, who were compared with 208 patients with all-cause sepsis. Three nonlinear markers, including the ratio of standard deviation derived from the Poincaré plot, sample entropy, and approximate entropy and four linear features, including mode of beat-to-beat interval, acceleration capacity, deceleration capacity, and the proportion of consecutive RR intervals that differ by more than 50 ms, were all statistically significant (p < 0.05) between the coronavirus disease 2019 and all-cause sepsis cohorts. The three nonlinear features and acceleration capacity, deceleration capacity, and beat-to-beat interval (mode) were statistically significant (p < 0.05) when comparing pairwise analysis among the combinations of survivors and nonsurvivors between the coronavirus disease 2019 and sepsis cohorts. Temporal analysis of the main markers showed low variability across the 5 days of analysis compared with sepsis patients.Conclusions and relevanceIn this descriptive statistical study, heart rate variability measures were found to be statistically different across critically ill patients infected with severe acute respiratory syndrome coronavirus 2 and distinct from bacterial sepsis.

Project description:Background: Aspirin, with its pleiotropic effects such as anti-inflammatory and anti-platelet aggregation, has been widely used for anti-inflammatory, analgesic, and cardiovascular diseases. However, the association between the use of aspirin before the intensive care unit (ICU) and clinical outcomes in critically ill patients with acute kidney injury (AKI) is unknown. Methods: Patients with AKI in this retrospective observational study were selected from the Marketplace for Medical Information in Intensive Care IV (MIMIC-IV). The association between aspirin intervention and 30-day mortality was assessed using Cox proportional hazards model. Logistic regression models were used to assess the association of aspirin intervention with the risks of intracranial hemorrhage, gastrointestinal bleeding and blood transfusion. The propensity score matching (PSM) method was adopted to balance the baseline variables. Sensitivity analysis was performed to validate the results by multiple interpolations for the missing data. Results: The study included 4237 pre-ICU aspirin users and 9745 non-users. In multivariate models, we found a decreased risk of mortality in those who received aspirin before ICU compared to those who did not (30-day:hazard ratio [HR], 0.70; 95% CI, 0.62-0.79; p < 0.001; 90-day:HR, 0.70; 95% CI, 0.63-0.77, p < 0.001; 180-day:HR, 0.72; 95%CI,0.65-0.79, p < 0.001). This benefit was consistent in the post-PSM analyses, sensitivity analyses, and subgroup analyses. Moreover, aspirin intervention was associated with a reduced risk of intracranial hemorrhage and gastrointestinal bleeding (HR, 0.16; 95% CI, 0.10-0.25; p < 0.001; HR, 0.59; 95% CI, 0.38-0.88, p = 0.012) after being adjusted by relating covariates, whereas with a increased risk of blood transfusion (HR, 1.28; 95% CI, 1.16-1.46; p < 0.001). Conclusion: Patients with AKI treated with aspirin before ICU admission might have reduced 30-day, 90-day and 180-day mortality without increasing the risk of intracranial hemorrhage (ICH) or gastrointestinal bleeding, but may increase the risk of transfusion.

Project description:IntroductionThe COVID-19 pandemic has been associated with significant variability in acute kidney injury (AKI) incidence, leading to concerns regarding patient heterogeneity. The study's primary objective was a cluster analysis, to identify homogeneous subgroups of patients (clusters) using baseline characteristics, including inflammatory biomarkers. The secondary objectives were the comparisons of MAKE-90 and mortality between the different clusters at three months.MethodsThis retrospective single-center study was conducted in the Medical Intensive Care Unit of the University Hospital of Clermont-Ferrand, France. Baseline data, clinical and biological characteristics on ICU admission, and outcomes at day 90 were recorded. The primary outcome was the risk of major adverse kidney events at 90 days (MAKE-90). Clusters were determined using hierarchical clustering on principal components approach based on admission characteristics, biomarkers and serum values of immune dysfunction and kidney function.ResultsIt included consecutive adult patients admitted between March 20, 2020 and February 28, 2021 for severe COVID-19. A total of 149 patients were included in the study. Three clusters were identified of which two were fully described (cluster 3 comprising 2 patients). Cluster 1 comprised 122 patients with fewer organ dysfunctions, moderate immune dysfunction, and was associated with reduced mortality and a lower incidence of MAKE-90. Cluster 2 comprised 25 patients with greater disease severity, immune dysfunction, higher levels of suPAR and L-FABP/U Creat, and greater organ support requirement, incidence of AKI, day-90 mortality and MAKE-90.ConclusionsThis study identified two clusters of severe COVID-19 patients with distinct biological characteristics and renal event risks. Such clusters may help facilitate the identification of targeted populations for future clinical trials. Also, it may help to understand the significant variability in AKI incidence observed in COVID-19 patients.

Project description:BackgroundRisk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.MethodsThis retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.ResultsA total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.ConclusionsOur results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients' risk for ICU delirium. Classification of patients' risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

Project description:ObjectivesICU admission delays can negatively affect patient outcomes, but emergency department volume and boarding times may also affect these decisions and associated patient outcomes. We sought to investigate the effect of emergency department and ICU capacity strain on ICU admission decisions and to examine the effect of emergency department boarding time of critically ill patients on in-hospital mortality.DesignA retrospective cohort study.SettingSingle academic tertiary care hospital.PatientsAdult critically ill emergency department patients for whom a consult for medical ICU admission was requested, over a 21-month period.InterventionsNone.Measurements and main resultsPatient data, including severity of illness (Mortality Probability Model III on Admission), outcomes of mortality and persistent organ dysfunction, and hourly census reports for the emergency department, for all ICUs and all adult wards were compiled. A total of 854 emergency department requests for ICU admission were logged, with 455 (53.3%) as "accept" and 399 (46.7%) as "deny" cases, with median emergency department boarding times 4.2 hours (interquartile range, 2.8-6.3 hr) and 11.7 hours (3.2-20.3 hr) and similar rates of persistent organ dysfunction and/or death 41.5% and 44.6%, respectively. Those accepted were younger (mean ± SD, 61 ± 17 vs 65 ± 18 yr) and more severely ill (median Mortality Probability Model III on Admission score, 15.3% [7.0-29.5%] vs 13.4% [6.3-25.2%]) than those denied admission. In the multivariable model, a full medical ICU was the only hospital-level factor significantly associated with a lower probability of ICU acceptance (odds ratio, 0.55 [95% CI, 0.37-0.81]). Using propensity score analysis to account for imbalances in baseline characteristics between those accepted or denied for ICU admission, longer emergency department boarding time after consult was associated with higher odds of mortality and persistent organ dysfunction (odds ratio, 1.77 [1.07-2.95]/log10 hour increase).ConclusionsICU admission decisions for critically ill emergency department patients are affected by medical ICU bed availability, though higher emergency department volume and other ICU occupancy did not play a role. Prolonged emergency department boarding times were associated with worse patient outcomes, suggesting a need for improved throughput and targeted care for patients awaiting ICU admission.

Project description:BackgroundPlatelets have been involved in both immune surveillance and host defense against severe infection. To date, whether platelet phenotype or other hemostasis components could be associated with predisposition to sepsis in critical illness remains unknown. The aim of this work was to identify platelet markers that could predict sepsis occurrence in critically ill injured patients.MethodsThis single-center, prospective, observational, 7-month study was based on a cohort of 99 non-infected adult patients admitted to ICUs for elective cardiac surgery, trauma, acute brain injury, and post-operative prolonged ventilation and followed up during ICU stay. Clinical characteristics and severity score (SOFA) were recorded on admission. Platelet activation markers, including fibrinogen binding to platelets, platelet membrane P-selectin expression, plasma soluble CD40L, and platelet-leukocytes aggregates were assayed by flow cytometry at admission and 48 h later, and then at the time of sepsis diagnosis (Sepsis-3 criteria) and 7 days later for sepsis patients. Hospitalization data and outcomes were also recorded.MethodsOf the 99 patients, 19 developed sepsis after a median time of 5 days. These patients had a higher SOFA score at admission; levels of fibrinogen binding to platelets (platelet-Fg) and of D-dimers were also significantly increased compared to the other patients. Levels 48 h after ICU admission no longer differed between the two patient groups. Platelet-Fg % was an independent predictor of sepsis (P = 0.0031). By ROC curve analysis, cutoff point for Platelet-Fg (AUC = 0.75) was 50%. In patients with a SOFA cutoff of 8, the risk of sepsis reached 87% when Platelet-Fg levels were above 50%. Patients with sepsis had longer ICU and hospital stays and higher death rate.ConclusionsPlatelet-bound fibrinogen levels assayed by flow cytometry within 24 h of ICU admission help identifying critically ill patients at risk of developing sepsis.

Project description:BackgroundRecognition of carbapenem-resistant gram-negative bacteria (CR-GNB) carriage is frequently delayed, which increases the risk of subsequent infection and transmission. Previously, we developed a scoring system to identify CR-GNB carriage upon intensive care unit (ICU) admission. Although the ICU-CARB score showed satisfactory performance, it has not been externally validated. In this study, therefore, we externally validated the ICU-CARB score.MethodsIn the previous article, we introduced a risk-scoring system that incorporated seven key variables: neurological disease, high-risk department history, length of stay ≥ 14 days, ICU history, invasive mechanical ventilation, gastrointestinal tube placement, and carbapenem usage. To externally validate the ICU-CARB score, we conducted a study involving patients admitted to the ICUs of four tertiary hospitals between January 2021 and December 2023. Patients from three hospitals were grouped into Cohort I (n = 815) and those from the fourth hospital into Cohort II (n = 1602). Model calibration, discrimination, and performance were then assessed.ResultsA total of 2417 patients were included, among which 289 (12%) carried CR-GNB upon ICU admission. Neurological disease, high-risk department history and length of stay ≥ 14 days were still 3 most important contributing factors in the scoring system. The ICU-CARB score exhibited high calibration, with an area under the receiver operating characteristic curve of 0.825 (95% confidence interval [CI], 0.778-0.873) for Cohort I and 0.823 (95% CI, 0.791-0.855) for Cohort II. The ICU-CARB score showed a highly positive association with CR-GNB carriage in both cohort I (C = 0.315; P < 0.001) and Cohort II (C = 0.381; P < 0.001).ConclusionsDespite differences in patient population characteristics, the ICU-CARB score for CR-GNB carriage upon ICU admission exhibited good discrimination in external validation, supporting its potential generalizability to other ICU settings.

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MRNA-based SARS-CoV-2 vaccination is associated with reduced ICU admission rate and disease severity in critically ill COVID-19 patients treated in Switzerland.

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mRNA-based SARS-CoV-2 vaccination is associated with reduced ICU admission rate and disease severity in critically ill COVID-19 patients treated in Switzerland.

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